Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Bhowmik, Arghya; Berecibar, Maitane; Casas‐Cabanas, Montse; Csänyi, Gábor; Dominko, Robert; Hermansson, Kersti; Palacin, M. R.; Stein, Helge S.; Vegge, Tejs

doi:10.1002/aenm.202102698

Cited by 45 publications

(33 citation statements)

References 216 publications

(254 reference statements)

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“…In this article, we lay out the challenges in the state‐of‐the‐art simulation approaches and examine how the new paradigm of machine learning assisted simulation techniques could help to unravel the fundamental understanding of SEI evolution. [ 28,29 ]…”

Section: Introductionmentioning

confidence: 99%

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Diddens

Appiah

Mabrouk

et al. 2022

Adv Materials Inter

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The solid electrolyte interphase (SEI) is a complex passivation layer that forms in situ on many battery electrodes such as lithium‐intercalated graphite or lithium metal anodes. Its essential function is to prevent the electrolyte from continuous electrochemical degradation, while simultaneously allowing ions to pass through, thus constituting an electronically insulating, but ionically conducting material. Its properties crucially affect the overall performance and aging of a battery cell. Despite decades of intense research, understanding the SEI's precise formation mechanism, structure, composition, and evolution remains a conundrum. State‐of‐the‐art computational modeling techniques are powerful tools to gain additional insights, although confronted with a trade‐off between accuracy and accessible time‐ and length scales. In this review, it is discussed how recent advances in data‐driven models, especially the development of fast and accurate surrogate simulators and deep generative models, can work with physics‐based and physics‐informed approaches to enable the next generation of breakthroughs in this field. Machine learning‐enhanced multiscale models can provide new pathways to inverse the design of interphases with desired properties.

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Section: Introductionmentioning

confidence: 99%

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Diddens

Appiah

Mabrouk

et al. 2022

Adv Materials Inter

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“…[38,39] Traditional single-scale models must be combined to form multi-scale workflows, for example, through generative deep learning. An overview of the potential impact of these techniques is given in (Bhowmik et al in this issue [2] ). Multi-scale modelling techniques are currently being developed, for example, to optimize real and virtual electrode microstructures [40] and to study the effects of the fabrication process on cell performance [41] and electrode surface film growth.…”

Section: Current Statusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…This paper summarizes the roadmap developed by the always BATTERY 2030+ consortium and is complemented by a number of articles in this special issue, including also one paper regarding the state-of-the-art. [2][3][4][5][6][7][8][9][10][11] The market for high-energy-density rechargeable batteries is currently dominated by the lithium-ion (Li-ion) battery (LIB), which performs well in most applications and has decreased dramatically in cost. However, current generation LIBs are approaching their performance limits.…”

Section: Introductionmentioning

confidence: 99%

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A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+

Amici

Asinari

Ayerbe

et al. 2022

Advanced Energy Materials

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123

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This roadmap presents the transformational research ideas proposed by “BATTERY 2030+,” the European large‐scale research initiative for future battery chemistries. A “chemistry‐neutral” roadmap to advance battery research, particularly at low technology readiness levels, is outlined, with a time horizon of more than ten years. The roadmap is centered around six themes: 1) accelerated materials discovery platform, 2) battery interface genome, with the integration of smart functionalities such as 3) sensing and 4) self‐healing processes. Beyond chemistry related aspects also include crosscutting research regarding 5) manufacturability and 6) recyclability. This roadmap should be seen as an enabling complement to the global battery roadmaps which focus on expected ultrahigh battery performance, especially for the future of transport. Batteries are used in many applications and are considered to be one technology necessary to reach the climate goals. Currently the market is dominated by lithium‐ion batteries, which perform well, but despite new generations coming in the near future, they will soon approach their performance limits. Without major breakthroughs, battery performance and production requirements will not be sufficient to enable the building of a climate‐neutral society. Through this “chemistry neutral” approach a generic toolbox transforming the way batteries are developed, designed and manufactured, will be created.

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“…Li-ion batteries (LIBs) dominate the secondary electrochemical energy storage market for both mobile (electric vehicle) and stationary (grid storage) applications. State of the art in battery research focus on deducing parameters that are key to battery performance and developing design principles accordingly [1,2,3]. LIB performance, durability and reliability depend on broad range of multi-scale phenomena [4].…”

Section: Introductionmentioning

confidence: 99%

Gaussian process sensitivity analysis of a capacity fade model for lithium-ion batteries

Appiah

Busk

Vegge

et al. 2022

Preprint

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Accurate battery models are important to maximise the utility of batteries which are ubiquitous in modern society from smartphones to electric vehicles. They act as digital twin of batteries and help in expeditious design optimization as well as help us explore interplay of electro- chemical phenomena. Here we present a sensitivity analysis of a pseudo-two-dimensional battery model coupled with a capacity fade model based on the formation of solid electrolyte interphase and the corresponding irreversible charge loss for Li-ion batteries. The proposed method is based on training an inexpensive differentiable surrogate Gaussian process regression model on observed input-output pairs and analysing the surrogate model to learn about the global and local sensitivities of the original system. Our results show that the proposed method is able to identify the most sensitive input parameters globally and that the same method can be used to explore local sensitivities around speci�c sets of inputs providing insights to the governing electrochemical process. In addition, we found a strong correlation between the growth of the solid electrolyte interphase and the irreversible charge loss especially at low current rates.

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Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Cited by 45 publications

References 216 publications

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+

Gaussian process sensitivity analysis of a capacity fade model for lithium-ion batteries

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